## pval_cutoff: 0.05
## lfc_cutoff: 1
## low_counts_cutoff: 10
General statistics
# Number of samples
length(counts_data)
## [1] 6
# Number of genes
nrow(counts_data)
## [1] 49315
# Total counts
colSums(counts_data)
## SRR13535276 SRR13535278 SRR13535280 SRR13535300 SRR13535302 SRR13535304
## 5442647 5284506 6692745 6783939 5245017 3380080
Create DDS objects
# Create DESeqDataSet object
dds <- get_DESeqDataSet_obj(counts_data, ~ treatment)
## [1] TRUE
## [1] TRUE
## [1] "DESeqDataSet object of length 49315 with 0 metadata columns"
## [1] "DESeqDataSet object of length 13961 with 0 metadata columns"
colData(dds)
## DataFrame with 6 rows and 25 columns
## Assay Type AvgSpotLen Bases BioProject BioSample Bytes Center Name Consent DATASTORE filetype DATASTORE provider DATASTORE region Experiment treatment GEO_Accession (exp) Instrument LibraryLayout LibrarySelection LibrarySource Organism Platform label ReleaseDate Sample Name source_name SRA Study
## <character> <numeric> <numeric> <character> <character> <numeric> <character> <character> <character> <character> <character> <character> <factor> <character> <character> <character> <character> <character> <character> <character> <factor> <POSIXct> <character> <character> <character>
## SRR13535276 RNA-Seq 300 8225466000 PRJNA694971 SAMN17588686 3252113587 GEO public fastq,sra gs,ncbi,s3 gs.US,ncbi.public,s3.. SRX9943360 A GSM5043430 Illumina HiSeq 2500 PAIRED cDNA TRANSCRIPTOMIC Mus musculus ILLUMINA in space without gravity 2021-09-09 GSM5043430 C2C12 proliferating .. SRP303354
## SRR13535278 RNA-Seq 300 9203426700 PRJNA694971 SAMN17588684 3619152333 GEO public fastq,sra gs,ncbi,s3 gs.US,ncbi.public,s3.. SRX9943362 A GSM5043433 Illumina HiSeq 2500 PAIRED cDNA TRANSCRIPTOMIC Mus musculus ILLUMINA in space without gravity 2021-09-09 GSM5043433 C2C12 proliferating .. SRP303354
## SRR13535280 RNA-Seq 300 9323939700 PRJNA694971 SAMN17588682 3735905901 GEO public fastq,sra gs,ncbi,s3 gs.US,ncbi.public,s3.. SRX9943364 A GSM5043436 Illumina HiSeq 2500 PAIRED cDNA TRANSCRIPTOMIC Mus musculus ILLUMINA in space without gravity 2021-09-09 GSM5043436 C2C12 proliferating .. SRP303354
## SRR13535300 RNA-Seq 300 12820015200 PRJNA694971 SAMN17587361 5047533646 GEO public fastq,sra gs,ncbi,s3 gs.US,ncbi.public,s3.. SRX9943384 E GSM5043471 Illumina HiSeq 2500 PAIRED cDNA TRANSCRIPTOMIC Mus musculus ILLUMINA on land 2021-09-09 GSM5043471 C2C12 proliferating .. SRP303354
## SRR13535302 RNA-Seq 300 12499917600 PRJNA694971 SAMN17587359 4941074444 GEO public fastq,sra gs,ncbi,s3 gs.US,ncbi.public,s3.. SRX9943386 E GSM5043475 Illumina HiSeq 2500 PAIRED cDNA TRANSCRIPTOMIC Mus musculus ILLUMINA on land 2021-09-09 GSM5043475 C2C12 proliferating .. SRP303354
## SRR13535304 RNA-Seq 300 7150086300 PRJNA694971 SAMN17587357 2845819297 GEO public fastq,sra gs,ncbi,s3 gs.US,ncbi.public,s3.. SRX9943388 E GSM5043478 Illumina HiSeq 2500 PAIRED cDNA TRANSCRIPTOMIC Mus musculus ILLUMINA on land 2021-09-09 GSM5043478 C2C12 proliferating .. SRP303354
Sample-to-sample comparisons
# Transform data (blinded rlog)
rld <- get_transformed_data(dds)
PCA plot
pca <- rld$pca
pca_df <- cbind(as.data.frame(colData(dds)) %>% rownames_to_column(var = 'name'), pca$x)
summary(pca)
## Importance of components:
## PC1 PC2 PC3 PC4 PC5 PC6
## Standard deviation 42.7426 37.4963 27.3551 20.29918 19.16927 6.947e-14
## Proportion of Variance 0.3837 0.2953 0.1572 0.08655 0.07719 0.000e+00
## Cumulative Proportion 0.3837 0.6791 0.8363 0.92281 1.00000 1.000e+00
ggplot(pca_df, aes(x = PC1, y = PC2, color = label)) +
geom_point() +
geom_text(aes(label = name), position = position_nudge(y = -2), show.legend = F, size = 3) +
scale_color_manual(values = colors_default) +
scale_x_continuous(expand = c(0.2, 0))
Correlation heatmap
pheatmap(
cor(rld$matrix),
annotation_col = as.data.frame(colData(dds)) %>% select(label),
color = brewer.pal(8, 'YlOrRd')
)
Wald test results
# DE analysis using Wald test
dds_full <- DESeq(dds)
colData(dds_full)
## DataFrame with 6 rows and 26 columns
## Assay Type AvgSpotLen Bases BioProject BioSample Bytes Center Name Consent DATASTORE filetype DATASTORE provider DATASTORE region Experiment treatment GEO_Accession (exp) Instrument LibraryLayout LibrarySelection LibrarySource Organism Platform label ReleaseDate Sample Name source_name SRA Study sizeFactor
## <character> <numeric> <numeric> <character> <character> <numeric> <character> <character> <character> <character> <character> <character> <factor> <character> <character> <character> <character> <character> <character> <character> <factor> <POSIXct> <character> <character> <character> <numeric>
## SRR13535276 RNA-Seq 300 8225466000 PRJNA694971 SAMN17588686 3252113587 GEO public fastq,sra gs,ncbi,s3 gs.US,ncbi.public,s3.. SRX9943360 A GSM5043430 Illumina HiSeq 2500 PAIRED cDNA TRANSCRIPTOMIC Mus musculus ILLUMINA in space without gravity 2021-09-09 GSM5043430 C2C12 proliferating .. SRP303354 0.983327
## SRR13535278 RNA-Seq 300 9203426700 PRJNA694971 SAMN17588684 3619152333 GEO public fastq,sra gs,ncbi,s3 gs.US,ncbi.public,s3.. SRX9943362 A GSM5043433 Illumina HiSeq 2500 PAIRED cDNA TRANSCRIPTOMIC Mus musculus ILLUMINA in space without gravity 2021-09-09 GSM5043433 C2C12 proliferating .. SRP303354 1.201268
## SRR13535280 RNA-Seq 300 9323939700 PRJNA694971 SAMN17588682 3735905901 GEO public fastq,sra gs,ncbi,s3 gs.US,ncbi.public,s3.. SRX9943364 A GSM5043436 Illumina HiSeq 2500 PAIRED cDNA TRANSCRIPTOMIC Mus musculus ILLUMINA in space without gravity 2021-09-09 GSM5043436 C2C12 proliferating .. SRP303354 1.114408
## SRR13535300 RNA-Seq 300 12820015200 PRJNA694971 SAMN17587361 5047533646 GEO public fastq,sra gs,ncbi,s3 gs.US,ncbi.public,s3.. SRX9943384 E GSM5043471 Illumina HiSeq 2500 PAIRED cDNA TRANSCRIPTOMIC Mus musculus ILLUMINA on land 2021-09-09 GSM5043471 C2C12 proliferating .. SRP303354 1.515920
## SRR13535302 RNA-Seq 300 12499917600 PRJNA694971 SAMN17587359 4941074444 GEO public fastq,sra gs,ncbi,s3 gs.US,ncbi.public,s3.. SRX9943386 E GSM5043475 Illumina HiSeq 2500 PAIRED cDNA TRANSCRIPTOMIC Mus musculus ILLUMINA on land 2021-09-09 GSM5043475 C2C12 proliferating .. SRP303354 0.802284
## SRR13535304 RNA-Seq 300 7150086300 PRJNA694971 SAMN17587357 2845819297 GEO public fastq,sra gs,ncbi,s3 gs.US,ncbi.public,s3.. SRX9943388 E GSM5043478 Illumina HiSeq 2500 PAIRED cDNA TRANSCRIPTOMIC Mus musculus ILLUMINA on land 2021-09-09 GSM5043478 C2C12 proliferating .. SRP303354 0.599689
# Wald test results
res <- results(
dds_full,
contrast = c('treatment', condition, control),
alpha = pval_cutoff
)
res
## log2 fold change (MLE): treatment A vs E
## Wald test p-value: treatment A vs E
## DataFrame with 13961 rows and 6 columns
## baseMean log2FoldChange lfcSE stat pvalue padj
## <numeric> <numeric> <numeric> <numeric> <numeric> <numeric>
## ENSMUSG00000025900 2.61498 -4.962524 2.553251 -1.943610 0.0519425 NA
## ENSMUSG00000098104 6.17973 -0.339872 0.953765 -0.356348 0.7215799 NA
## ENSMUSG00000033845 78.73893 -0.139777 0.484080 -0.288747 0.7727748 0.919754
## ENSMUSG00000102275 1.93535 -0.248188 1.752974 -0.141581 0.8874108 NA
## ENSMUSG00000025903 64.57553 -0.218443 0.653769 -0.334129 0.7382824 0.907746
## ... ... ... ... ... ... ...
## ENSMUSG00000061654 1.27223 0.825925 3.184504 0.259357 0.7953595 NA
## ENSMUSG00000079834 14.09960 1.903612 1.045337 1.821050 0.0685992 0.344032
## ENSMUSG00000095041 158.20403 0.140872 0.612231 0.230095 0.8180177 0.938169
## ENSMUSG00000063897 20.52396 -0.458058 0.853403 -0.536743 0.5914454 0.836773
## ENSMUSG00000095742 7.49461 -0.218513 1.062907 -0.205580 0.8371187 NA
mcols(res)
## DataFrame with 6 rows and 2 columns
## type description
## <character> <character>
## baseMean intermediate mean of normalized c..
## log2FoldChange results log2 fold change (ML..
## lfcSE results standard error: trea..
## stat results Wald statistic: trea..
## pvalue results Wald test p-value: t..
## padj results BH adjusted p-values
summary(res)
##
## out of 13961 with nonzero total read count
## adjusted p-value < 0.05
## LFC > 0 (up) : 239, 1.7%
## LFC < 0 (down) : 104, 0.74%
## outliers [1] : 169, 1.2%
## low counts [2] : 3519, 25%
## (mean count < 8)
## [1] see 'cooksCutoff' argument of ?results
## [2] see 'independentFiltering' argument of ?results
plotDispEsts(dds_full)
Summary details
# Upregulated genes (LFC > 0)
res_sig_df %>% filter(log2FoldChange > 0)
# Downregulated genes (LFC < 0)
res_sig_df %>% filter(log2FoldChange < 0)
# Outliers (pvalue and padj are NA)
res[which(is.na(res$pvalue)), ]
## log2 fold change (MLE): treatment A vs E
## Wald test p-value: treatment A vs E
## DataFrame with 169 rows and 6 columns
## baseMean log2FoldChange lfcSE stat pvalue padj
## <numeric> <numeric> <numeric> <numeric> <numeric> <numeric>
## ENSMUSG00000067780 221.9873 -2.582501 1.46924 -1.757707 NA NA
## ENSMUSG00000025981 118.3309 -0.529115 1.15229 -0.459187 NA NA
## ENSMUSG00000038349 75.0131 -4.200908 1.26705 -3.315497 NA NA
## ENSMUSG00000026024 42.2767 -3.720642 1.35025 -2.755516 NA NA
## ENSMUSG00000085842 18.6638 6.046114 3.47619 1.739291 NA NA
## ... ... ... ... ... ... ...
## ENSMUSG00000005871 313.558 -0.607219 1.04277 -0.582315 NA NA
## ENSMUSG00000044595 35.330 1.595573 1.67162 0.954505 NA NA
## ENSMUSG00000024597 269.224 -1.425211 1.09095 -1.306397 NA NA
## ENSMUSG00000033417 193.435 -1.252626 1.07430 -1.165996 NA NA
## ENSMUSG00000064340 120.809 0.332759 1.16820 0.284847 NA NA
# Low counts (only padj is NA)
res[which(is.na(res$padj) & !is.na(res$pvalue)), ]
## log2 fold change (MLE): treatment A vs E
## Wald test p-value: treatment A vs E
## DataFrame with 3519 rows and 6 columns
## baseMean log2FoldChange lfcSE stat pvalue padj
## <numeric> <numeric> <numeric> <numeric> <numeric> <numeric>
## ENSMUSG00000025900 2.61498 -4.962524 2.553251 -1.943610 0.0519425 NA
## ENSMUSG00000098104 6.17973 -0.339872 0.953765 -0.356348 0.7215799 NA
## ENSMUSG00000102275 1.93535 -0.248188 1.752974 -0.141581 0.8874108 NA
## ENSMUSG00000090031 4.72015 2.124519 1.341590 1.583583 0.1132885 NA
## ENSMUSG00000098201 1.51075 1.802079 1.856699 0.970583 0.3317561 NA
## ... ... ... ... ... ... ...
## ENSMUSG00000065947 5.24532 0.261970 1.04998 0.249499 0.802975 NA
## ENSMUSG00000064366 2.13604 -0.772911 2.41976 -0.319417 0.749410 NA
## ENSMUSG00000064372 6.86230 -0.401501 1.08759 -0.369167 0.712003 NA
## ENSMUSG00000061654 1.27223 0.825925 3.18450 0.259357 0.795359 NA
## ENSMUSG00000095742 7.49461 -0.218513 1.06291 -0.205580 0.837119 NA
Shrunken LFC results
plotMA(res)
# Shrunken LFC results
res_shrunken <- lfcShrink(
dds_full,
coef = str_c('treatment_', condition, '_vs_', control),
type = 'apeglm'
)
res_shrunken
## log2 fold change (MAP): treatment A vs E
## Wald test p-value: treatment A vs E
## DataFrame with 13961 rows and 5 columns
## baseMean log2FoldChange lfcSE pvalue padj
## <numeric> <numeric> <numeric> <numeric> <numeric>
## ENSMUSG00000025900 2.61498 -0.1300702 0.514057 0.0519425 NA
## ENSMUSG00000098104 6.17973 -0.0711113 0.441210 0.7215799 NA
## ENSMUSG00000033845 78.73893 -0.0702829 0.346909 0.7727748 0.919754
## ENSMUSG00000102275 1.93535 -0.0175174 0.472511 0.8874108 NA
## ENSMUSG00000025903 64.57553 -0.0783319 0.396773 0.7382824 0.907746
## ... ... ... ... ... ...
## ENSMUSG00000061654 1.27223 0.0194986 0.484854 0.7953595 NA
## ENSMUSG00000079834 14.09960 0.4149437 0.670691 0.0685992 0.344032
## ENSMUSG00000095041 158.20403 0.0563089 0.384739 0.8180177 0.938169
## ENSMUSG00000063897 20.52396 -0.1154234 0.438228 0.5914454 0.836773
## ENSMUSG00000095742 7.49461 -0.0384303 0.445851 0.8371187 NA
plotMA(res_shrunken)
mcols(res_shrunken)
## DataFrame with 5 rows and 2 columns
## type description
## <character> <character>
## baseMean intermediate mean of normalized c..
## log2FoldChange results log2 fold change (MA..
## lfcSE results posterior SD: treatm..
## pvalue results Wald test p-value: t..
## padj results BH adjusted p-values
summary(res_shrunken, alpha = pval_cutoff)
##
## out of 13961 with nonzero total read count
## adjusted p-value < 0.05
## LFC > 0 (up) : 239, 1.7%
## LFC < 0 (down) : 104, 0.74%
## outliers [1] : 169, 1.2%
## low counts [2] : 3519, 25%
## (mean count < 8)
## [1] see 'cooksCutoff' argument of ?results
## [2] see 'independentFiltering' argument of ?results
Summary details
# Upregulated genes (LFC > 0)
res_shrunken_sig_df %>% filter(log2FoldChange > 0)
# Downregulated genes (LFC < 0)
res_shrunken_sig_df %>% filter(log2FoldChange < 0)
# Outliers (pvalue and padj are NA)
res_shrunken[which(is.na(res_shrunken$pvalue)), ]
## log2 fold change (MAP): treatment A vs E
## Wald test p-value: treatment A vs E
## DataFrame with 169 rows and 5 columns
## baseMean log2FoldChange lfcSE pvalue padj
## <numeric> <numeric> <numeric> <numeric> <numeric>
## ENSMUSG00000067780 221.9873 -0.2417310 0.561932 NA NA
## ENSMUSG00000025981 118.3309 -0.0799970 0.459272 NA NA
## ENSMUSG00000038349 75.0131 -3.2042335 1.586687 NA NA
## ENSMUSG00000026024 42.2767 -0.4662874 0.878920 NA NA
## ENSMUSG00000085842 18.6638 0.0641954 0.495755 NA NA
## ... ... ... ... ... ...
## ENSMUSG00000005871 313.558 -0.1092319 0.458138 NA NA
## ENSMUSG00000044595 35.330 0.1222203 0.494985 NA NA
## ENSMUSG00000024597 269.224 -0.2527030 0.531987 NA NA
## ENSMUSG00000033417 193.435 -0.2253146 0.511858 NA NA
## ENSMUSG00000064340 120.809 0.0514497 0.455209 NA NA
# Low counts (only padj is NA)
res_shrunken[which(is.na(res_shrunken$padj) & !is.na(res_shrunken$pvalue)), ]
## log2 fold change (MAP): treatment A vs E
## Wald test p-value: treatment A vs E
## DataFrame with 3519 rows and 5 columns
## baseMean log2FoldChange lfcSE pvalue padj
## <numeric> <numeric> <numeric> <numeric> <numeric>
## ENSMUSG00000025900 2.61498 -0.1300702 0.514057 0.0519425 NA
## ENSMUSG00000098104 6.17973 -0.0711113 0.441210 0.7215799 NA
## ENSMUSG00000102275 1.93535 -0.0175174 0.472511 0.8874108 NA
## ENSMUSG00000090031 4.72015 0.2857734 0.575077 0.1132885 NA
## ENSMUSG00000098201 1.51075 0.1227917 0.495432 0.3317561 NA
## ... ... ... ... ... ...
## ENSMUSG00000065947 5.24532 0.0481426 0.445980 0.802975 NA
## ENSMUSG00000064366 2.13604 -0.0302867 0.481739 0.749410 NA
## ENSMUSG00000064372 6.86230 -0.0666771 0.452667 0.712003 NA
## ENSMUSG00000061654 1.27223 0.0194986 0.484854 0.795359 NA
## ENSMUSG00000095742 7.49461 -0.0384303 0.445851 0.837119 NA
Visualizing results
Heatmaps
# Plot normalized counts (z-scores)
pheatmap(counts_sig_norm[2:7],
color = brewer.pal(8, 'YlOrRd'),
cluster_rows = T,
show_rownames = F,
annotation_col = as.data.frame(colData(dds)) %>% select(label),
border_color = NA,
fontsize = 10,
scale = 'row',
fontsize_row = 10,
height = 20)
# Plot log-transformed counts
pheatmap(counts_sig_log[2:7],
color = rev(brewer.pal(8, 'RdYlBu')),
cluster_rows = T,
show_rownames = F,
annotation_col = as.data.frame(colData(dds)) %>% select(label),
border_color = NA,
fontsize = 10,
fontsize_row = 10,
height = 20)
# Plot log-transformed counts (top 24 DE genes)
pheatmap(counts_sig_log %>% filter(ensembl_gene_id %in% (res_sig_df %>% head(24))$ensembl_gene_id) %>% select(-ensembl_gene_id) %>% column_to_rownames(var = 'mgi_symbol'),
color = rev(brewer.pal(8, 'RdYlBu')),
cluster_rows = T,
show_rownames = T,
annotation_col = as.data.frame(colData(dds)) %>% select(label),
fontsize = 10,
fontsize_row = 10,
height = 20)
Volcano plots
# Unshrunken LFC
res_df %>%
mutate(
sig_threshold = if_else(
padj < pval_cutoff & abs(log2FoldChange) >= lfc_cutoff,
if_else(log2FoldChange > 0, 'DE-up', 'DE-down'),
'non-DE'
)
) %>%
filter(!is.na(sig_threshold)) %>%
ggplot() +
geom_point(aes(x = log2FoldChange, y = -log10(padj), colour = sig_threshold)) +
scale_color_manual(values = c('blue', 'red', 'gray')) +
xlab('log2 fold change') +
ylab('-log10 adjusted p-value')
# Shrunken LFC
res_shrunken_df %>%
mutate(
sig_threshold = if_else(
padj < pval_cutoff & abs(log2FoldChange) >= lfc_cutoff,
if_else(log2FoldChange > 0, 'DE-up', 'DE-down'),
'non-DE'
)
) %>%
filter(!is.na(sig_threshold)) %>%
ggplot() +
geom_point(aes(x = log2FoldChange, y = -log10(padj), colour = sig_threshold)) +
scale_color_manual(values = c('blue', 'red', 'gray')) +
xlab('log2 fold change') +
ylab('-log10 adjusted p-value')
GSEA (all)
Hallmark genesets
# Shrunken LFC
get_fgsea_res(rank_lfc, mm_h) %>% plot_enrichment_table(rank_lfc, mm_h)
# Wald stat
get_fgsea_res(rank_stat, mm_h) %>% plot_enrichment_table(rank_stat, mm_h)
# Rank: sign(LFC) * -log10(pvalue)
get_fgsea_res(rank_pval, mm_h) %>% plot_enrichment_table(rank_pval, mm_h)
GO biological process
# Shrunken LFC
get_fgsea_res(rank_lfc, mm_c5_bp) %>% plot_enrichment_table(rank_lfc, mm_c5_bp)
# Wald stat
get_fgsea_res(rank_stat, mm_c5_bp) %>% plot_enrichment_table(rank_stat, mm_c5_bp)
# Rank: sign(LFC) * -log10(pvalue)
get_fgsea_res(rank_pval, mm_c5_bp) %>% plot_enrichment_table(rank_pval, mm_c5_bp)
GO cellular component
# Shrunken LFC
get_fgsea_res(rank_lfc, mm_c5_cc) %>% plot_enrichment_table(rank_lfc, mm_c5_cc)
# Wald stat
get_fgsea_res(rank_stat, mm_c5_cc) %>% plot_enrichment_table(rank_stat, mm_c5_cc)
# Rank: sign(LFC) * -log10(pvalue)
get_fgsea_res(rank_pval, mm_c5_cc) %>% plot_enrichment_table(rank_pval, mm_c5_cc)
GO molecular function
# Shrunken LFC
get_fgsea_res(rank_lfc, mm_c5_mf) %>% plot_enrichment_table(rank_lfc, mm_c5_mf)
# Wald stat
get_fgsea_res(rank_stat, mm_c5_mf) %>% plot_enrichment_table(rank_stat, mm_c5_mf)
# Rank: sign(LFC) * -log10(pvalue)
get_fgsea_res(rank_pval, mm_c5_mf) %>% plot_enrichment_table(rank_pval, mm_c5_mf)
GSEA (DE)
Hallmark genesets
# Shrunken LFC
get_fgsea_res(rank_lfc, mm_h) %>% plot_enrichment_table(rank_lfc, mm_h)
# Wald stat
get_fgsea_res(rank_stat, mm_h) %>% plot_enrichment_table(rank_stat, mm_h)
# Rank: sign(LFC) * -log10(pvalue)
get_fgsea_res(rank_pval, mm_h) %>% plot_enrichment_table(rank_pval, mm_h)
GO biological process
# Shrunken LFC
get_fgsea_res(rank_lfc, mm_c5_bp) %>% plot_enrichment_table(rank_lfc, mm_c5_bp)
# Wald stat
get_fgsea_res(rank_stat, mm_c5_bp) %>% plot_enrichment_table(rank_stat, mm_c5_bp)
# Rank: sign(LFC) * -log10(pvalue)
get_fgsea_res(rank_pval, mm_c5_bp) %>% plot_enrichment_table(rank_pval, mm_c5_bp)
GO cellular component
# Shrunken LFC
get_fgsea_res(rank_lfc, mm_c5_cc) %>% plot_enrichment_table(rank_lfc, mm_c5_cc)
# Wald stat
get_fgsea_res(rank_stat, mm_c5_cc) %>% plot_enrichment_table(rank_stat, mm_c5_cc)
# Rank: sign(LFC) * -log10(pvalue)
get_fgsea_res(rank_pval, mm_c5_cc) %>% plot_enrichment_table(rank_pval, mm_c5_cc)
GO molecular function
# Shrunken LFC
get_fgsea_res(rank_lfc, mm_c5_mf) %>% plot_enrichment_table(rank_lfc, mm_c5_mf)
# Wald stat
get_fgsea_res(rank_stat, mm_c5_mf) %>% plot_enrichment_table(rank_stat, mm_c5_mf)
# Rank: sign(LFC) * -log10(pvalue)
get_fgsea_res(rank_pval, mm_c5_mf) %>% plot_enrichment_table(rank_pval, mm_c5_mf)
System info
sessionInfo()
## R version 4.1.0 (2021-05-18)
## Platform: x86_64-conda-linux-gnu (64-bit)
## Running under: CentOS Linux 7 (Core)
##
## Matrix products: default
## BLAS/LAPACK: /home/chan/mRNA_seq_pipeline/.snakemake/conda/9a19315a020c824d12f8055f7c009b0f/lib/libopenblasp-r0.3.18.so
##
## locale:
## [1] LC_CTYPE=en_US.UTF-8 LC_NUMERIC=C LC_TIME=en_US.UTF-8 LC_COLLATE=en_US.UTF-8 LC_MONETARY=en_US.UTF-8 LC_MESSAGES=en_US.UTF-8 LC_PAPER=en_US.UTF-8 LC_NAME=C LC_ADDRESS=C LC_TELEPHONE=C LC_MEASUREMENT=en_US.UTF-8 LC_IDENTIFICATION=C
##
## attached base packages:
## [1] stats4 stats graphics grDevices utils datasets methods base
##
## other attached packages:
## [1] fgsea_1.20.0 RColorBrewer_1.1-2 pheatmap_1.0.12 DESeq2_1.34.0 SummarizedExperiment_1.24.0 Biobase_2.54.0 MatrixGenerics_1.6.0 matrixStats_0.61.0 GenomicRanges_1.46.0 GenomeInfoDb_1.30.0 IRanges_2.28.0 S4Vectors_0.32.0 BiocGenerics_0.40.0 scales_1.1.1 forcats_0.5.1 stringr_1.4.0 dplyr_1.0.7 purrr_0.3.4 readr_2.1.1 tidyr_1.1.4 tibble_3.1.6 ggplot2_3.3.5 tidyverse_1.3.1
##
## loaded via a namespace (and not attached):
## [1] colorspace_2.0-2 ellipsis_0.3.2 XVector_0.34.0 fs_1.5.1 rstudioapi_0.13 farver_2.1.0 bit64_4.0.5 mvtnorm_1.1-3 AnnotationDbi_1.56.1 fansi_0.4.2 apeglm_1.16.0 lubridate_1.8.0 xml2_1.3.3 splines_4.1.0 cachem_1.0.6 geneplotter_1.72.0 knitr_1.35 jsonlite_1.7.2 broom_0.7.10 annotate_1.72.0 dbplyr_2.1.1 png_0.1-7 compiler_4.1.0 httr_1.4.2 backports_1.4.0 assertthat_0.2.1 Matrix_1.3-4 fastmap_1.1.0 cli_3.1.0 htmltools_0.5.2 tools_4.1.0 coda_0.19-4 gtable_0.3.0 glue_1.5.1 GenomeInfoDbData_1.2.7 fastmatch_1.1-3 Rcpp_1.0.7 bbmle_1.0.24 cellranger_1.1.0 jquerylib_0.1.4 vctrs_0.3.8 Biostrings_2.62.0 xfun_0.28 rvest_1.0.2 lifecycle_1.0.1 XML_3.99-0.8 MASS_7.3-54 zlibbioc_1.40.0 vroom_1.5.7 hms_1.1.1 parallel_4.1.0 yaml_2.2.1 memoise_2.0.1 gridExtra_2.3 emdbook_1.3.12 bdsmatrix_1.3-4 stringi_1.7.6 RSQLite_2.2.8 highr_0.9 genefilter_1.76.0 BiocParallel_1.28.0 rlang_0.4.12 pkgconfig_2.0.3 bitops_1.0-7 evaluate_0.14 lattice_0.20-45 labeling_0.4.2 bit_4.0.4 tidyselect_1.1.1 plyr_1.8.6 magrittr_2.0.1 R6_2.5.1 generics_0.1.1 DelayedArray_0.20.0 DBI_1.1.1 pillar_1.6.4 haven_2.4.3 withr_2.4.3 survival_3.2-13 KEGGREST_1.34.0 RCurl_1.98-1.5 modelr_0.1.8 crayon_1.4.2 utf8_1.2.2 tzdb_0.2.0 rmarkdown_2.11 locfit_1.5-9.4 grid_4.1.0 readxl_1.3.1 data.table_1.14.2 blob_1.2.2 reprex_2.0.1 digest_0.6.29 xtable_1.8-4 numDeriv_2016.8-1.1 munsell_0.5.0